Name
Abstract | ||
|---|---|---|
We present two probabilistic models to estimate the risk of introducing infectious diseases into previously unaffected countries/regions by infective travellers. We analyse two distinct situations, one dealing with a directly transmitted infection (measles in Italy in 2017) and one dealing with a vector-borne infection (Zika virus in Rio de Janeiro, which may happen in the future). To calculate the risk in the first scenario, we used a simple, nonhomogeneous birth process. The second model proposed in this paper provides a way to calculate the probability that local mosquitoes become infected by the arrival of a single infective traveller during his/her infectiousness period. The result of the risk ofmeasles invasion of Italy was of 93% and the result of the risk of Zika virus invasion of Rio de Janeiro was of 22%. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1155/2018/6289681 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Field | DocType | Volume |
Zika virus,Demography,Measles,Computer science,Artificial intelligence,Machine learning | Journal | 2018 |
ISSN | Citations | PageRank |
1748-670X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marcos Amaku | 1 | 9 | 2.78 |
| Francisco Antonio Bezerra Coutinho | 2 | 8 | 3.50 |
| Margaret Armstrong | 3 | 0 | 0.34 |
| Eduardo Massad | 4 | 33 | 12.99 |